LASR-Callum

nika-sft-tulu-toolcall-80-20-both-think-tokens-loss

README

License: other

Training data

LASR-Callum/2026-08-03-tulu-toolcall-80-20-mixture — 1,791 examples, 1,496,873 tokens, 79.98% TULU3 / 20.02% agentic.

Hyperparameters

Table

LoRA	r=32, α=64, dropout=0.05
target modules	`model\.language_model\..*\.(q_proj\|k_proj\|v_proj\|o_proj\|gate_proj\|up_proj\|down_proj)$`
precision	bf16 (not 4-bit: bitsandbytes does not reliably cover this model's linear-attention layers)
epochs	1
batch	1 × grad-accum 16
lr	1e-4, cosine → 0, warmup 3%
max_seq_len	4096 (sibling arms use 2048; see below)
packing	off
loss masking	assistant tokens only
steps	112
loss	0.853 → 0.762 over 22 points logged every 5 steps. Note the level: the retracted full-sequence run started at 2.753. Masked loss sees only assistant tokens, which the base model already predicts far better than prompt tokens, so the ~3x lower start is itself corroboration that the mask changed which tokens are trained.
wall clock	1h23m (112 steps at ~40.6 s/step) on 1×H100 80GB

Sequence length: why 4096

At 2048 only 80.4% of the agentic corpus survives and 11 of its <tool_call> spans are severed, inside exactly the long conversations the tool calls live in. Truncation is silent — nothing errors. At 4096 the agentic corpus is whole: longest row 3,989 tokens, 0 rows truncated, 0 of its 92 <tool_call> spans severed.

Label-mask verification

Run before training, on real mixture rows through the real collator, with an independent parser that re-derives each conversation's role regions rather than re-running the masking code (scratch/verify_mask.py):

120 rows sampled (72 agentic, 48 TULU3), deliberately over-weighting long multi-turn and tool-call-heavy rows — 251,669 tokens.
0 supervised tokens on system, user or tool content, and 0 outside any assistant span.
95 multi-turn rows checked; the window reaches 572 assistant turns and all 572 are supervised (not just each row's first turn).
Supervised fraction 68.3% of tokens on the sample, 74.2% over the full mixture. Assistant content is 69.4% of the same text by character — the ceiling the mask could reach, and what it tracks. High because this corpus is mostly assistant text, not because the mask is inert.

Why this is deprecated: 95.6% of think blocks are empty

Every assistant turn carries a think block and 2,449 of 2,561 are empty — and under assistant-only masking those empty blocks are supervised. That is the intended experiment (an invariant tag structure across the whole corpus), but it is also the documented empty-think collapse by construction. If this adapter stops reasoning, check this first, not last.

Status

Trained and published (public). Not yet evaluated — no agentic-misalignment or capability numbers exist for it yet.

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Model Details

Model Provider

LASR-Callum

Model Tree

Base

Qwen/Qwen3.6-27B

Adapter

this model

Input Modalities

TextImageVideo

Output Modalities

Text

Supported Functionality